Generic function for the computation of the Kolmogorov distance of two distributions
Generic function for the computation of the Kolmogorov distance d_k of two distributions P and Q where the distributions are defined on a finite-dimensional Euclidean space (R^m, B^m) with B^m the Borel-sigma-algebra on R^m. The Kolmogorov distance is defined as
d_k(P,Q)=\sup{|P({y in R^m | y <= x})-Q({y in R^m | y <= x})| | x in R^m}
where ≤ is coordinatewise on R^m.
KolmogorovDist(e1, e2, ...) ## S4 method for signature 'AbscontDistribution,AbscontDistribution' KolmogorovDist(e1,e2) ## S4 method for signature 'AbscontDistribution,DiscreteDistribution' KolmogorovDist(e1,e2) ## S4 method for signature 'DiscreteDistribution,AbscontDistribution' KolmogorovDist(e1,e2) ## S4 method for signature 'DiscreteDistribution,DiscreteDistribution' KolmogorovDist(e1,e2) ## S4 method for signature 'numeric,UnivariateDistribution' KolmogorovDist(e1, e2) ## S4 method for signature 'UnivariateDistribution,numeric' KolmogorovDist(e1, e2) ## S4 method for signature 'AcDcLcDistribution,AcDcLcDistribution' KolmogorovDist(e1, e2)
e1 |
object of class |
e2 |
object of class |
... |
further arguments to be used in particular methods (not in package distrEx) |
Kolmogorov distance of e1 and e2
Kolmogorov distance of two absolutely continuous univariate distributions which is computed using a union of a (pseudo-)random and a deterministic grid.
Kolmogorov distance of two discrete univariate distributions.
The distance is attained at some point of the union of the supports
of e1 and e2.
Kolmogorov distance of absolutely continuous and discrete
univariate distributions. It is computed using a union of
a (pseudo-)random and a deterministic grid in combination
with the support of e2.
Kolmogorov distance of discrete and absolutely continuous
univariate distributions. It is computed using a union of
a (pseudo-)random and a deterministic grid in combination
with the support of e1.
Kolmogorov distance between (empirical) data and a univariate
distribution. The computation is based on ks.test.
Kolmogorov distance between (empirical) data and a univariate
distribution. The computation is based on ks.test.
Kolmogorov distance of mixed discrete and absolutely continuous
univariate distributions. It is computed using a union of
the discrete part, a (pseudo-)random and
a deterministic grid in combination
with the support of e1.
Matthias Kohl Matthias.Kohl@stamats.de,
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de
Huber, P.J. (1981) Robust Statistics. New York: Wiley.
Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.
KolmogorovDist(Norm(), UnivarMixingDistribution(Norm(1,2),Norm(0.5,3),
mixCoeff=c(0.2,0.8)))
KolmogorovDist(Norm(), Td(10))
KolmogorovDist(Norm(mean = 50, sd = sqrt(25)), Binom(size = 100))
KolmogorovDist(Pois(10), Binom(size = 20))
KolmogorovDist(Norm(), rnorm(100))
KolmogorovDist((rbinom(50, size = 20, prob = 0.5)-10)/sqrt(5), Norm())
KolmogorovDist(rbinom(50, size = 20, prob = 0.5), Binom(size = 20, prob = 0.5))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.